Like, suppose you think that Eliezer’s credences on his biggest claims are literally 2x higher than they should be, even for claims where he’s 90% confident. This is a huge hit in terms of Bayes points; if that’s how you determine deference, and you believe he’s 2x off, then plausibly you should defer to him less than you do to the median EA. But when it comes to grantmaking, for example, a cost-effectiveness factor of 2x is negligible given the other uncertainties involved—this should very rarely move you from a yes to no, or vice versa.
Such differences are crucial for many of the most important grant areas IME, because they are areas where you are trading off multiple high-stakes concerns. E.g. in nuclear policy all the strategies on offer have arguments that they might lead to nuclear war or worse war. On AI alignment there are multiple such tradeoffs and people embracing strategies to push the same variable in opposite directions with high stakes on both sides.
I haven’t thought much about nuclear policy, so I can’t respond there. But at least in alignment, I expect that pushing on variables where there’s less than a 2x difference between the expected positive and negative effects of changing that variable is not a good use of time for altruistically-motivated people.
(By contrast, upweighting or downweighting Eliezer’s opinions by a factor of 2 could lead to significant shifts in expected value, especially for people who are highly deferential. The specific thing I think doesn’t make much difference is deferring to a version of Eliezer who’s 90% confident about something, versus deferring to the same extent to a version of Eliezer who’s 45% confident in the same thing.)
My more general point, which doesn’t hinge on the specific 2x claim, is that naive conversions between metrics of calibration and deferential weightings are a bad idea, and that a good way to avoid naive conversions is to care a lot more about innovative thinking than calibration when deferring.
Such differences are crucial for many of the most important grant areas IME, because they are areas where you are trading off multiple high-stakes concerns. E.g. in nuclear policy all the strategies on offer have arguments that they might lead to nuclear war or worse war. On AI alignment there are multiple such tradeoffs and people embracing strategies to push the same variable in opposite directions with high stakes on both sides.
I haven’t thought much about nuclear policy, so I can’t respond there. But at least in alignment, I expect that pushing on variables where there’s less than a 2x difference between the expected positive and negative effects of changing that variable is not a good use of time for altruistically-motivated people.
(By contrast, upweighting or downweighting Eliezer’s opinions by a factor of 2 could lead to significant shifts in expected value, especially for people who are highly deferential. The specific thing I think doesn’t make much difference is deferring to a version of Eliezer who’s 90% confident about something, versus deferring to the same extent to a version of Eliezer who’s 45% confident in the same thing.)
My more general point, which doesn’t hinge on the specific 2x claim, is that naive conversions between metrics of calibration and deferential weightings are a bad idea, and that a good way to avoid naive conversions is to care a lot more about innovative thinking than calibration when deferring.